OpenRaiser/Intern-Atlas huggingface dataset:
TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph
Venue: arXiv cs.IT 2015
Year: 2015

Location awareness is now becoming a vital requirement for many practical applications. In this paper, we consider passive localization of multiple targets with one transmitter and several receivers based on time of arrival (TOA) measurements. Existing studies assume that positions of receivers are perfectly known. However, in practice, receivers' positions might be inaccurate, which leads to localization error of targets. We propose factor graph (FG)-based belief propagation (BP) algorithms to locate the passive targets and improve the position accuracy of receivers simultaneously. Due to the nonlinearity of the likelihood function, messages on the FG cannot be derived in closed form. We propose both sample-based and parametric methods to solve this problem. In the sample-based BP algorithm, particle swarm optimization is employed to reduce the number of particles required to represent messages. In parametric BP algorithm, the nonlinear terms in messages are linearized, which results in closed-form Gaussian message passing on FG. The Bayesian Cramer-Rao bound (BCRB) for passive targets localization with uncertain receivers is derived to evaluate the performance of the proposed algorithms. Simulation results show that both the sample-based and parametric BP algorithms outperform the conventional method and attain the proposed BCRB. Receivers' positions can also be improved via the proposed BP algorithms. Although the parametric BP algorithm performs slightly worse than the sample-based BP method, it could be more attractive in practical applications due to the significantly lower computational complexity.
Blind Recovery of Sparse Signals from Subsampled Convolution
Venue: arXiv cs.IT 2015
Year: 2015

Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors tailored to specific application have been developed in practical applications. In particular, sparsity models have provided promising priors. However, in spite of empirical success of these methods in many applications, existing analyses are rather limited in two main ways: by disparity between the theoretical assumptions on the signal and/or measurement model versus practical setups; or by failure to provide a performance guarantee for parameter values within the optimal regime defined by the information theoretic limits. In particular, it has been shown that a naive sparsity model is not a strong enough prior for identifiability in the blind deconvolution problem. Instead, in addition to sparsity, we adopt a conic constraint, which enforces spectral flatness of the signals. Under this prior, we provide an iterative algorithm that achieves guaranteed performance in blind deconvolution at near optimal sample complexity. Numerical results show the empirical performance of the iterative algorithm agrees with the performance guarantee.
Refined analysis of RGHWs of code pairs coming from Garcia-Stichtenoth's second tower
Venue: arXiv cs.IT 2015
Year: 2015

Asymptotically good sequences of ramp secret sharing schemes were given in [Asymptotically good ramp secret sharing schemes, arXiv:1502.05507] by using one-point algebraic geometric codes defined from asymptotically good towers of function fields. Their security is given by the relative generalized Hamming weights of the corresponding codes. In this paper we demonstrate how to obtain refined information on the RGHWs when the codimension of the codes is small. For general codimension, we give an improved estimate for the highest RGHW.
Diffusing Private Data over Networks
Venue: arXiv cs.DS 2015
Year: 2015

The emergence of social and technological networks has enabled rapid sharing of data and information. This has resulted in significant privacy concerns where private information can be either leaked or inferred from public data. The problem is significantly harder for social networks where we may reveal more information to our friends than to strangers. Nonetheless, our private information can still leak to strangers as our friends are their friends and so on. In order to address this important challenge, in this paper, we present a privacy-preserving mechanism that enables private data to be diffused over a network. In particular, whenever a user wants to access another users' data, the proposed mechanism returns a differentially private response that ensures that the amount of private data leaked depends on the distance between the two users in the network. While allowing global statistics to be inferred by users acting as analysts, our mechanism guarantees that no individual user, or a group of users, can harm the privacy guarantees of any other user. We illustrate our mechanism with two examples: one on synthetic data where the users share their GPS coordinates; and one on a Facebook ego-network where a user shares her infection status.
Feature-based Attention in Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural networks (CNNs) have proven effective for image processing tasks, such as object recognition and classification. Recently, CNNs have been enhanced with concepts of attention, similar to those found in biology. Much of this work on attention has focused on effective serial spatial processing. In this paper, I introduce a simple procedure for applying feature-based attention (FBA) to CNNs and compare multiple implementation options. FBA is a top-down signal applied globally to an input image which aides in detecting chosen objects in cluttered or noisy settings. The concept of FBA and the implementation details tested here were derived from what is known (and debated) about biological object- and feature-based attention. The implementations of FBA described here increase performance on challenging object detection tasks using a procedure that is simple, fast, and does not require additional iterative training. Furthermore, the comparisons performed here suggest that a proposed model of biological FBA (the "feature similarity gain model") is effective in increasing performance.
On Binary Embedding using Circulant Matrices
Venue: arXiv cs.DS 2015
Year: 2015

Binary embeddings provide efficient and powerful ways to perform operations on large scale data. However binary embedding typically requires long codes in order to preserve the discriminative power of the input space. Thus binary coding methods traditionally suffer from high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure allows us to use Fast Fourier Transform algorithms to speed up the computation. For obtaining $k$-bit binary codes from $d$-dimensional data, this improves the time complexity from $O(dk)$ to $O(d\log{d})$, and the space complexity from $O(dk)$ to $O(d)$. We study two settings, which differ in the way we choose the parameters of the circulant matrix. In the first, the parameters are chosen randomly and in the second, the parameters are learned using the data. For randomized CBE, we give a theoretical analysis comparing it with binary embedding using an unstructured random projection matrix. The challenge here is to show that the dependencies in the entries of the circulant matrix do not lead to a loss in performance. In the second setting, we design a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. In both the settings, we show by extensive experiments that the CBE approach gives much better performance than the state-of-the-art approaches if we fix a running time, and provides much faster computation with negligible performance degradation if we fix the number of bits in the embedding.
A dense subgraph based algorithm for compact salient image region detection
Venue: arXiv cs.CV 2015
Year: 2015

We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image. To compute the salient regions, the model first obtains a saliency map using random walks on a Markov chain. Next, k-dense subgraphs are detected to further enhance the salient regions in the image. Dense subgraphs convey more information about local graph structure than simple centrality measures. To generate the Markov chain, intensity and color features of an image in addition to region compactness is used. For evaluating the proposed model, we do extensive experiments on benchmark image data sets. The proposed method performs comparable to well-known algorithms in salient region detection.
Bezier curves and surfaces based on modified Bernstein polynomials
Venue: arXiv cs.GR 2015
Year: 2015

In this paper, we use the blending functions of Bernstein polynomials with shifted knots for construction of Bezier curves and surfaces. We study the nature of degree elevation and degree reduction for Bezier Bernstein functions with shifted knots. Parametric curves are represented using these modified Bernstein basis and the concept of total positivity is applied to investigate the shape properties of the curve. We get Bezier curve defined on [0, 1] when we set the parameter α=βto the value 0. We also present a de Casteljau algorithm to compute Bernstein Bezier curves and surfaces with shifted knots. The new curves have some properties similar to Bezier curves. Furthermore, some fundamental properties for Bernstein Bezier curves and surfaces are discussed.
Solving non-linear Horn clauses using a linear solver
Venue: arXiv cs.LO 2015
Year: 2015

Developing an efficient non-linear Horn clause solver is a challenging task since the solver has to reason about the tree structures rather than the linear ones as in a linear solver. In this paper we propose an incremental approach to solving a set of non-linear Horn clauses using a linear Horn clause solver. We achieve this by interleaving a program transformation and a linear solver. The program transformation is based on the notion of tree dimension, which we apply to trees corresponding to Horn clause derivations. The dimension of a tree is a measure of its non-linearity -- for example a linear tree (whose nodes have at most one child) has dimension zero while a complete binary tree has dimension equal to its height. A given set of Horn clauses $P$ can be transformed into a new set of clauses $P^k$ (whose derivation trees are the subset of $P$'s derivation trees with dimension at most $k$). We start by generating $P^k$ with $k=0$, which is linear by definition, then pass it to a linear solver. If $P^k$ has a solution $M$, and is a solution to $P$ then $P$ has a solution $M$. If $M$ is not a solution of $P$, we plugged $M$ to $P^{(k+1)}$ which again becomes linear and pass it to the solver and continue successively for increasing value of $k$ until we find a solution to $P$ or resources are exhausted. Experiment on some Horn clause verification benchmarks indicates that this is a promising approach for solving a set of non-linear Horn clauses using a linear solver. It indicates that many times a solution obtained for some under-approximation $P^k$ of $P$ becomes a solution for $P$ for a fairly small value of $k$.
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation
Venue: arXiv cs.CV 2015
Year: 2015

Integrating higher level visual and linguistic interpretations is at the heart of human intelligence. As automatic visual category recognition in images is approaching human performance, the high level understanding in the dynamic spatiotemporal domain of videos and its translation into natural language is still far from being solved. While most works on vision-to-text translations use pre-learned or pre-established computational linguistic models, in this paper we present an approach that uses vision alone to efficiently learn how to translate into language the video content. We discover, in simple form, the story played by main actors, while using only visual cues for representing objects and their interactions. Our method learns in a hierarchical manner higher level representations for recognizing subjects, actions and objects involved, their relevant contextual background and their interaction to one another over time. We have a three stage approach: first we take in consideration features of the individual entities at the local level of appearance, then we consider the relationship between these objects and actions and their video background, and third, we consider their spatiotemporal relations as inputs to classifiers at the highest level of interpretation. Thus, our approach finds a coherent linguistic description of videos in the form of a subject, verb and object based on their role played in the overall visual story learned directly from training data, without using a known language model. We test the efficiency of our approach on a large scale dataset containing YouTube clips taken in the wild and demonstrate state-of-the-art performance, often superior to current approaches that use more complex, pre-learned linguistic knowledge.
Analysis of SVN Repositories for Remote Access
Venue: arXiv cs.OH 2015
Year: 2015

Software Evolution is considered to be essential and challenging characteristic in the field of software engineering. Version control system is an incremental versions tracking system, introduced to avoid unnecessary overwriting of files such as programming code, web pages and records. It also helps to decrease the confusion affected by duplicate or outdated data. In this proposed research SVN repository is maintained and analyzed for msitone.wikispaces.com to minimize the efforts as well as resources for the future users. We have used two semester data for the analysis purpose that is observed SVN repository. The result shows that, implementing the SVN repositories are helpful for maintenance of the Wikispaces as it also reduce the cost, time and efforts for their evolution. Whereas without implementing the SVN repositories Wikispaces were just supposed to be building the house by putting each brick from start.
Testable Design of Repeaterless Low Swing On-Chip Interconnect
Venue: arXiv cs.AR 2015
Year: 2015

Repeaterless low swing interconnects use mixed signal circuits to achieve high performance at low power. When these interconnects are used in large scale and high volume digital systems their testability becomes very important. This paper discusses the testability of low swing repeaterless on-chip interconnects with equalization and clock synchronization. A capacitively coupled transmitter with a weak driver is used as the transmitter. The receiver samples the low swing input data at the center of the data eye and converts it to rail to rail levels and also synchronizes the data to the receiver's clock domain. The system is a mixed signal circuit and the digital components are all scan testable. For the analog section, just a DC test has a fault coverage of 50% of the structural faults. Simple techniques allow integration of the analog components into the digital scan chain increasing the coverage to 74%. Finally, a BIST with low overhead enhances the coverage to 95% of the structural faults. The design and simulations have been done in UMC 130 nm CMOS technology.
An Empirical Comparison of the Summarization Power of Graph Clustering Methods
Venue: arXiv cs.IR 2015
Year: 2015

How do graph clustering techniques compare with respect to their summarization power? How well can they summarize a million-node graph with a few representative structures? Graph clustering or community detection algorithms can summarize a graph in terms of coherent and tightly connected clusters. In this paper, we compare and contrast different techniques: METIS, Louvain, spectral clustering, SlashBurn and KCBC, our proposed k-core-based clustering method. Unlike prior work that focuses on various measures of cluster quality, we use vocabulary structures that often appear in real graphs and the Minimum Description Length (MDL) principle to obtain a graph summary per clustering method. Our main contributions are: (i) Formulation: We propose a summarization-based evaluation of clustering methods. Our method, VOG-OVERLAP, concisely summarizes graphs in terms of their important structures which lead to small edge overlap, and large node/edge coverage; (ii) Algorithm: we introduce KCBC, a graph decomposition technique, in the heart of which lies the k-core algorithm (iii) Evaluation: We compare the summarization power of five clustering techniques on large real graphs, and analyze their compression performance, summary statistics and runtimes.
GradNets: Dynamic Interpolation Between Neural Architectures
Venue: arXiv cs.LG 2015
Year: 2015

In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Venue: arXiv cs.CV 2015
Year: 2015

We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as "cute baby". To capture the two-factor structure of our ANP semantics as well as to overcome annotation noise and ambiguity, we propose a novel factorized CNN model which learns separate representations for adjectives and nouns but optimizes the classification performance over their product. Our experiments on the publicly available SentiBank dataset show that our model significantly outperforms not only independent ANP classifiers on unseen ANPs and on retrieving images of novel ANPs, but also image captioning models which capture word semantics from co-occurrence of natural text; the latter turn out to be surprisingly poor at capturing the sentiment evoked by pure visual experience. That is, our factorized ANP CNN not only trains better from noisy labels, generalizes better to new images, but can also expands the ANP vocabulary on its own.
Applying Social Media Intelligence for Predicting and Identifying On-line Radicalization and Civil Unrest Oriented Threats
Venue: arXiv cs.CY 2015
Year: 2015

Research shows that various social media platforms on Internet such as Twitter, Tumblr (micro-blogging websites), Facebook (a popular social networking website), YouTube (largest video sharing and hosting website), Blogs and discussion forums are being misused by extremist groups for spreading their beliefs and ideologies, promoting radicalization, recruiting members and creating online virtual communities sharing a common agenda. Popular microblogging websites such as Twitter are being used as a real-time platform for information sharing and communication during planning and mobilization if civil unrest related events. Applying social media intelligence for predicting and identifying online radicalization and civil unrest oriented threats is an area that has attracted several researchers' attention over past 10 years. There are several algorithms, techniques and tools that have been proposed in existing literature to counter and combat cyber-extremism and predicting protest related events in much advance. In this paper, we conduct a literature review of all these existing techniques and do a comprehensive analysis to understand state-of-the-art, trends and research gaps. We present a one class classification approach to collect scholarly articles targeting the topics and subtopics of our research scope. We perform characterization, classification and an in-depth meta analysis meta-anlaysis of about 100 conference and journal papers to gain a better understanding of existing literature.
A Simple Algorithm For Replacement Paths Problem
Venue: arXiv cs.DS 2015
Year: 2015

Let G=(V,E)(|V|=n and |E|=m) be an undirected graph with positive edge weights. Let P_{G}(s, t) be a shortest s-t path in G. Let l be the number of edges in P_{G}(s, t). The \emph{Edge Replacement Path} problem is to compute a shortest s-t path in G\{e}, for every edge e in P_{G}(s, t). The \emph{Node Replacement Path} problem is to compute a shortest s-t path in G\{v}, for every vertex v in P_{G}(s, t). In this paper we present an O(T_{SPT}(G)+m+l^2) time and O(m+l^2) space algorithm for both the problems. Where, T_{SPT}(G) is the asymptotic time to compute a single source shortest path tree in G. The proposed algorithm is simple and easy to implement.
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
Venue: arXiv cs.CV 2015
Year: 2015

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.
Countering Social Engineering through Social Media: An Enterprise Security Perspective
Venue: arXiv cs.CY 2015
Year: 2015

The increasing threat of social engineers targeting social media channels to advance their attack effectiveness on company data has seen many organizations introducing initiatives to better understand these vulnerabilities. This paper examines concerns of social engineering through social media within the enterprise and explores countermeasures undertaken to stem ensuing risk. Also included is an analysis of existing social media security policies and guidelines within the public and private sectors.
Real-Time Anomaly Detection and Localization in Crowded Scenes
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto- encoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.